Stats

The automated trading fund manager made the following premises: the Savedbacktest456 strategy was used with a next-open20close breakout, 1ADR stop, and 2ADR target over 5 sessions, and was tested using costed vectorbt with training data from 2022-2024 and holdout data from 2025 to September 4. The prior CLI53 was audited and rejected as an execution proxy. A news scan found the link https://runway.adityaperswal.workers.dev/data/stock-bars. The Python calculation of 17 out of 41 trades, or 0.4146341463, showed a mean win of 5.46270406%, a mean loss of 2.96357664%, an expectancy of 0.5302470649%, a breakeven probability of 35.1706375%, and a raw quarter Kelly of 2.42666937%. The historical mean confidence interval was between -0.874362% and 1.958541%, which includes zero. No new trials or parameter sweep were conducted. The trade ledger only showed two operator-liquidation wins, and under an unrealistic independent and identically distributed assumption, the Wilson 95% probability was between 34.2380% and 100%. The empirical average loss was undefined, and the planned stop and target counts were zero, as found on the website https://runway.adityaperswal.workers.dev. Existing statistics, including a correlation of 0.78064 between FRO and STNG, and a simulated conditional 20-trade loss frequency of 38.17%, did not validate an independent edge. The conclusion was to approve a Kelly of 0. The selection, correlated trades, regime shift, and daily-bar fill uncertainty weakened the soundness of the point estimates. A counterexample of a positive expected payoff may be real, but it requires prospective independent net evidence rather than inferring certainty from the live 2 out of 2 trades. The decision was to retain the frozen train and holdout, and not to rebacktest during the trading run.

A news scan found the headline about pre-open dependence and uncertainty regarding a tanker pair being one concentrated theme. Data was collected from January 2025 to September 8, 2026, with 421 daily bars per name and no duplicates or bad data. The data was then paired to show the most recent 60 daily returns. The Pearson correlation between FRO and STNG is 0.78064075, which means they tend to move together. The correlation to the SPY index is 0.09613 for FRO and 0.02578 for STNG, which is a measure of how much they move with the overall market. The beta, or volatility measure, is 0.38501 for FRO and 0.07476 for STNG. ASPI has a correlation of 0.48753 with SPY and a beta of 3.12913. These numbers describe the current market state, but do not show cause and effect or investment strategy. In the past, holding FRO resulted in 17 wins out of 41, or a 41.4634% win rate, with an average win of 5.462704% and an average loss of 2.963577%. The return was 20.242565% and the maximum loss was 24.419466%. However, this is old data and not new evidence. Two live trades were successful, but since they were liquidated by the operator, it is not possible to estimate the win or loss rate or the efficiency of the stop-loss rule. There have been no live losses, so it is not possible to calculate the average loss. The high correlation between the two stocks and the unresolved payoff imply that there is no opportunity to improve the investment by diversifying. However, correlations can change due to shocks in the product or crude oil markets, so it is not safe to assume that the correlation will remain at 0.78. There are still gaps in the data, including selection and survivorship biases, serial dependence, and execution issues.

A news scan found the headline 'HUBB positive point estimate, unresolved confidence and regime coverage'. This refers to an analysis of the stock HUBB. The analysis used a method called bootstrap to estimate the confidence interval, which is a range of values within which a population parameter is likely to lie, and found that the mean 95% bootstrap confidence interval for HUBB is [-0.15619409475090745, 2.4420381181021766]%, which crosses zero. Every entry above 200 for the stock SPY and below regime N0.3 was considered. A daily analysis from 2026 using a statistical method called ordinary least squares, or OLS, found the following results: the sample size was 171, the daily alpha percentage was -0.028969605559672994, the market beta was 1.1197039983882822, and the r-squared value was 0.19447913987568277. Correlations with other stocks were also calculated: 0.5153584779443469 for WCC, 1 for HUBB, and 0.44099789101047054 for SPY. The source of this data is https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=WCC,HUBB,SPY&timeframe=1Day&feed=sip&adjustment=all. The OLS intercept was not abnormal, but the strategy return was affected by heteroskedasticity, which is a situation where the variance of the error term is not constant, and unmodeled factors, which means that the model did not account for all relevant factors. The stocks were selected for their current business relevance, and the analysis omitted other stocks that did not survive. The trading windows were clustered, and issuer news was not used as historical event labels. The analysis used a method called iid bootstrap, which understates dependence and misses unobserved tails. The frozen training data from 20/5 was negative, and 17 out of 18 training variants were negative across both stocks, with the sole positive result being HUBB20/10, which was not selected. The positive holdout point estimate cannot repair the training instability. The analysis requires prospectively frozen independent events and costed payoffs with a positive lower confidence interval, and does not involve retuning historical windows.

A chronological analysis of 2026 holdout data from a preregistration number 1385 found that the mean 95% bootstrap confidence interval, which is a measure of the range of possible values, was between -2.1918036650942527% and 3.061479355934164%, and this range crosses zero. Every entry above the SPY 200, which is a stock market index, and below regime N0.3, a specific market condition, was considered. A daily analysis for 2026 using ordinary least squares, a statistical method, found the following results: the sample size was 171, the daily alpha percentage, which measures excess return, was 0.1179413135865879%, the market beta, which measures volatility, was 2.0673477285826802, and the r-squared, which measures the fit of the model, was 0.3398358653773208. The correlations between stocks were as follows: WCC and itself was 1, WCC and HUBB was 0.5153584779443469, and WCC and SPY was 0.5829544282165809. This data was sourced from https://runway.adityaperswal.workers.dev/data/stock-bars?symbols=WCC,HUBB,SPY&timeframe=1Day&feed=sip&adjustment=all. The intercept of the ordinary least squares model was not abnormal, but the strategy return was affected by heteroskedasticity, which is a measure of the variance of the data, and unmodeled factors, which means that no inference can be made from the coefficient alone. Stocks were selected based on current business relevance, and survivors were omitted, while others were not considered. Trading windows and issuer news were not labeled as historical events. The iid bootstrap, a statistical method, understates dependence and misses unobserved tails. The frozen training data from 20/5 was negative, and 17 out of 18 training variants were negative across both stocks, with the sole positive result being HUBB 20/10, which was not selected. The positive holdout point estimate cannot repair the training instability. Prospective, independent events with costed payoffs and a positive lower confidence interval are required, rather than re-tuning historical windows.

A source at https://runway.adityaperswal.workers.dev/data/stock-bars was used for training. The training grid had lookback periods of 10, 20, and 40, and hold periods of 5, 10, and 20, with costs of 20 basis points per side. There were 9 rows of data for APD, which included information such as return percentage, drawdown percentage, win rate percentage, average win percentage, average loss percentage, sample size, mean percentage, and quarter Kelly percentage. The data was as follows: a lookback period of 10 and hold period of 5 had a return percentage of -8.954063612187165 and a drawdown percentage of 27.89129971036093, a lookback period of 10 and hold period of 10 had a return percentage of -30.317463956351787 and a drawdown percentage of 48.19066423191016, a lookback period of 10 and hold period of 20 had a return percentage of -5.188465269371554 and a drawdown percentage of 36.85076356695909, a lookback period of 20 and hold period of 5 had a return percentage of -7.375662785718904 and a drawdown percentage of 20.42229777243616, a lookback period of 20 and hold period of 10 had a return percentage of -12.432149251989376 and a drawdown percentage of 34.72148454626669, a lookback period of 20 and hold period of 20 had a return percentage of -21.791169010157667 and a drawdown percentage of 44.17412119283325, a lookback period of 40 and hold period of 5 had a return percentage of -11.75449386122178 and a drawdown percentage of 18.70927757466876, a lookback period of 40 and hold period of 10 had a return percentage of -15.90832655761591 and a drawdown percentage of 22.917039943337326, and a lookback period of 40 and hold period of 20 had a return percentage of -26.641487433706722 and a drawdown percentage of 31.341153566635406. No best setting was transferred to the holdout. A bootstrap analysis with 20,000 independent and identically distributed samples was performed, with a numpy seed of 2026090904, and the 95% confidence interval was found to be between -2.5336899222192355% and 1.772706274444286%. The sample size was small, and the experiment was limited to one industry and selected firms, with no independent helium experiments and no causal regression claimed. The baseline expectancy was negative, and the confidence interval crossed 0, resulting in a quarter Kelly of 0. This does not imply a lack of bullish fundamentals, but rather a failed momentum strategy.

A source at https://runway.adityaperswal.workers.dev/data/stock-bars was used to train a model called Frozen1346 with different settings. The model was trained with a lookback period of 10, 20, or 40 and a hold period of 5, 10, or 20, and costs of 20 basis points per side. The results of the training are shown in a list of 9 rows, each representing a different combination of lookback and hold periods. The list includes the return percentage, drawdown percentage, win rate percentage, average win percentage, average loss percentage, sample size, mean percentage, and quarter Kelly value for each combination. The return percentage ranged from -24.8046% to 8.3892%, and the quarter Kelly value was 0 for all combinations except one, which was 0.0081, and another which was 0.0294, and a third which was 0.0009. No best setting was transferred to the holdout set. A bootstrap analysis was performed with 20,000 independent and identically distributed samples, using a numpy seed of 2026090904, and the 95% confidence interval was found to be between -2.4286% and 1.2898%. The analysis has some limitations, including a small sample size, only one industry, and selected firms, and no causal regression is claimed. The baseline expectation is negative and the confidence interval crosses 0, which is why the quarter Kelly value is 0. This result should not be taken to mean that there are no bullish fundamentals, only that the momentum strategy did not work.

A wide-net screen found correlated high-beta infrastructure stocks, not independent bets. The screen used data from USdirectory13156 and found 12574 valid pairs with a median return of -0.808625%, a 10th percentile return of -3.832307%, and a 90th percentile return of 2.646877%. The screen excluded corporate action outliers like TAN and special formats like 544, and only considered stocks with a non-ETF price of at least 2 USD and an IEX volume of at least 100k. The chosen tickers were not random, but were matched against the SPY using ordinary least squares (OLS) regression, which is a statistical method to measure the relationship between variables. The results showed that the tickers SEI, LEU, and HUT had correlations with each other and with the SPY, with correlation coefficients ranging from 0.453 to 0.562. However, these correlations do not guarantee portfolio diversification, as different bottlenecks can affect the stocks. The soundness of the results is based on current-survivor selection and price-only OLS, without accounting for higher-order autocorrelation (HAC) or causal identification. The positive intercept of HUT is not a statistical alpha, meaning it is not a measure of the stock's performance. The holdout confidence intervals (CIs) from 1312 to 1322 all failed, indicating no edge or transfer of information from broad movements. The US screen attempted to cover all listings, but did not achieve complete instrument coverage, and targeted watchlists included 19 non-US names, Brent, BTC/ETH, and 10 venue cryptopairs, rather than a global census.

A fund manager analyzed data from a costed ledger with 1302 supplies and 24 natural closed trades, which included 5 wins and 19 losses, with a mean return of -2.204649% and a median return of -6.842921%. The 10th and 90th percentiles were -8.908747% and +16.735762%. Using a bootstrap method with 20,000 iterations and a seed of 2026090900, the manager found a 95% mean interval of -5.888270% to +1.969444%. This result is a counterexample to the idea that every possible edge has been disproven, but it is not evidence for an entry. The manager also analyzed the correlation between ASPI and SPY returns from 2025 to September 8, with a Pearson correlation of 0.393117 and an OLS slope with intercept of 2.443695. The descriptive beta exposes market-risk confounding, but does not estimate event alpha or customer acceptance. The sample excludes pre-listing dates and intraday/overnight executable prices. The manager's own supply ledger has only two operator-liquidation winners and no observed loss distribution. The conclusion is that a zero allocation to the frozen rule is valid, but not certain to be true in the future. The soundness of the conclusion is limited by the small sample size, dependence, non-stationarity, and assumed costs. No re-tuning was done.

The profitability of same-vintage companies has increased, according to data from the US Census Bureau. The data shows that profits for these companies were $359,388, $295,242, and $215,550 in millions of US dollars, with sales of $2,324,071, $2,130,066, and $1,980,333, resulting in profit margins of 15.463727%, 13.860697%, and 10.884533% for the second quarter, first quarter, and second quarter of the previous year, respectively. This calculation was done using Python arithmetic and reported quarterly nominal totals, not annualized returns. The retail sector had rounded ratios of 9.5893%, 5.8813%, and 5.5987%. Additionally, manufacturing inventory increased by 2.7373% and debt increased by 0.5842% quarter-over-quarter. The data also shows that coverage thresholds omit smaller firms, with manufacturing utilization rates of 56.4% and sales to total quarterly rates of 87.5%, and retail utilization rates of 49.7% and sales to total quarterly rates of 87.3%. The methodology used to calculate these numbers was updated on July 27, and the new methodology is different from the previous one, which may have caused the increase in profitability. The data does not provide enough information to determine the cause of the increase, and the soundness of the conclusion is limited by the comparability of the methods and the absence of causal identification. The observations from 15899 to 15902 and 15915 to 15916 are noted, but no exhaustive census of the sector, debt, or suppression is claimed.

A review of the complete supply ledger at https://runway.adityaperswal.workers.dev using numpy found the following results: FRO had a return of 3.805163%, STNG had a return of 0.752257%, the mean return was 2.278710%, and the gross profit and loss was 4.977287. There were 2 wins and 0 losses, with both operators liquidating their positions. The Wilson 95% win interval of 34.238-100% is an example of independent and identically distributed results, but this is not supported because the exits were influenced by the operators and the time period. The first assumption is that the observed wins are real, but a counterexample is that discretionary liquidation removed the original 6-8 week exposure. The second assumption is that a calibrated edge requires the distribution of wins and losses for the actual rule, but the loss distribution is not available, there is no benchmark for slippage, and the frequency of stops and targets is not observed. The valid conclusion is that there is insufficient calibration, not a 100% win forecast. The quarter Kelly criterion cannot be computed from the fund's own record. Earlier frozen costed holdouts provide stronger evidence for rejection, and loss magnitude should not be manufactured from an arbitrary stop. No holdings require a review.

A study of 1424 daily log returns from 2021 to September 4, 2026, found that MATX distribution and transport-factor regression do not identify weather alpha. The study used consolidated SIP adjustment data from MATX, SPY, and IYT, which can be found at https://runway.adityaperswal.workers.dev/data/stock-bars. The annual volatility was 39.31%, with percentiles of -6.25904%, -3.60672%, 0.118246%, 3.57557%, and 7.17046%. The correlations between MATX and SPY were 0.467525, and between MATX and IYT were 0.565051. A regression analysis using Numpy OLS found coefficients of 0.000589068, 0.199300, and 0.899655, with an R² of 0.322242. This analysis provides a descriptive factor fit for contemporaneous data, but does not identify prospective excess returns or causal weather relationships. The source data covers selected current tickers and total return-like adjusted prices, but not every delisted shipper. The commercial source data from July is lagged and cannot be regressed as daily September weather without invented observations. Financial payoff bootstrap was recorded separately, and no new edge was inferred.

A news scan found the text describing the performance of a trading fund. The fund analyzed 1255 matched daily log returns from September 2021 to September 4, 2026. The fund's annualized volatility was 38.3374 percent, with percentile returns of -6.54592, -3.82171, 0.106695, 3.88321, and 6.15356 percent. The correlations between the fund and other stocks, MOS, NTR, and SPY, were 0.631186, 0.678122, and 0.166138, respectively. A regression analysis, which is a statistical method to establish a relationship between variables, was performed using NumpyOLS, a library for numerical operations, with the equation CF, which refers to the trading fund, being equal to a constant plus SPY, MOS, and NTR, resulting in coefficients of 0.000897788, -0.163206, 0.284375, and 0.524489, with an R-squared value of 0.505259. This analysis is descriptive and does not imply causation or predict future performance. The fund also compared its performance to its peers, with a median absolute percentage difference of 0.21175 percent and a maximum difference of 2.68442 percent. On September 4, the fund's opening price was 133.74 on IEX, a stock exchange, and 135.43 on SIP, another stock exchange, with a closing price of 133.35 on both. The fund's ADR, which stands for Average Daily Return, was 4.857175 on SIP, exceeding the IEX ADR of 4.44475 by 9.27892 percent. The fund's data was adjusted for feed coverage, which refers to the availability of market data, and training signals, but does not account for dividend cash mechanics or execution quotes. The analysis has limitations, including the exclusion of delisted companies and missing historical global physical spread observations.

A study using Python and data from 2022 to September 4, 2026, analyzed the daily log returns of stocks GD, GE, and TXT. The study found that the correlation between GD and GE was 0.34985, between GD and TXT was 0.52545, and between GE and TXT was 0.47580. However, these correlations do not necessarily mean that one stock can be substituted for another due to shared macro and defense factors, as well as GE's corporate transformations. The daily returns of GD were also analyzed, with quantiles of -3.58259%, -1.88758%, 0.073469%, 2.00976%, and 3.72127% for the 1%, 5%, 50%, 95%, and 99% percentiles, respectively. The annualized sample volatility was 20.9136%. The study also found that the current selection of stocks and data coverage is limited, missing delisted firms and most global aircraft owners. A causal regression analysis could not be performed due to the lack of event surprise series and transaction-price microdata. A holdout test of 7 returns from observation 12763 resulted in an average return of -1.08743%, with a 95% mean interval of -1.99661% to 0.175079% based on 20000 bootstrap resamples. The study concluded that the numeric evidence supports a zero size position, rather than a probability for the political threat, and recorded full measured correlations, quantiles, and trade distributions from observations 12752 to 12776.

The automated trading fund manager made some observations based on data from a website. They looked at 9893 out of 9897 independent trade returns from https://runway.adityaperswal.workers.dev/data/stock-bars. Using a method called bootstrap, which involves randomly selecting data, they found the following results, seeded on September 8, 2026: for ATI, the mean return was 61.9048%, with a win rate of 3.61227% and a loss rate of 2.63703%, and a 95% confidence interval of -0.481668% to 3.058334%; for IREN, the mean return was 33.3333%, with a win rate of 2.77049% and a loss rate of 6.21184%, and a confidence interval of -6.556397% to 0.179634%. They also calculated a Quarter Kelly point estimate, which is a measure of the optimal fraction of wealth to bet, of 8.523625% for ATI and 0% for IREN. However, ATI is not authorized for 8.52% because it failed training, selection, and confidence interval tests, and is missing a signal. Both ATI and IREN have a 0% authorization. The manager also looked at their own ledger and found two operator liquidation wins, with a mean return of 2.278710% and no losses, but noted that this is not a representative sample of the entire population. The manager's reasoning is that there is no robust positive edge, which means there is no reliable advantage, so they are allocating zero funds. However, the soundness of this decision is uncertain due to limitations in the data analysis, such as not accounting for regime clusters, using current ticker selection, and having few trades and unobserved future losses. A strong counter case is that ATI's 2026 profit could survive higher costs, but the manager cannot retroactively select 2026 as the sole training period. They require prospective evidence from a held-out regime, rather than re-tuning the model.

The automated trading fund manager has analyzed BWEN's trades. Two sets of data, Observation9305 and backtest749, contain 12 closed trades from 2026. These trades were used to calculate the possible range of returns, which is between -9.561459% and 1.242761%. This range is based on 20,000 random samples of the 12 returns. However, this range is not a guarantee of future performance due to the small sample size and other factors. The probability of a win is estimated to be 0.25, with an average win of 8.465371% and an average loss of 8.003927%. The Kelly criterion, a formula used to determine the optimal bet size, is 1.05765. The live supply ledger shows two wins averaging 2.278710%, but no losses. The prior mean interval of -0.874362% to 1.958541% remains unresolved. The goal is to achieve a repeatable positive net return, but this has not been achieved yet. The fund manager requires more evidence of independent payoff before making any decisions.

A pre-open sample audit on September 7 found two discretionary wins, but these do not identify a sizing edge. The audit involved recalculating all supply trades from live query_trades in Python/numpy, which showed the following results: FRO had a return of 3.805162958%, STNG had a return of 0.752256770%, and the mean win was 2.278709864%. There were 2 trades with no observed losses. Both trades were exited by operator liquidation on September 4 after entries on September 2, and not due to strategy stop or target outcomes. The source of this information is the venue ledger at https://runway.adityaperswal.workers.dev, which shows no open or pending rows. An illustrative independent and identically distributed (IID) Wilson 95% lower win-probability bound of 34.2380% for 2 out of 2 trades already exposes uncertainty, and since the trades had shared tanker exposure and discretionary exits, this bound cannot be used as a calibrated probability. The average loss, execution slippage versus synchronous quotes, and stop or target hit frequencies are also unknown. The audit also found that freezing existing empirical evidence instead of treating repeated readings as new data showed the following results: FRO analysis had 456 trades with 41 mean confidence interval of [-0.874362, 1.958541]%, and aluminum had 615 out of 616 actual-ADR exits that were negative, as seen at https://runway.adityaperswal.workers.dev/data/stock-bars. These are distinct cohorts and not pooled experiments, and no fresh backtest is claimed. The conclusion is valid under the required numeric edge gate, with an approved size of 0, but its soundness is limited by finite selected histories and unmodeled 15-minute fills. A counterexample of profitable scarcity repricing ahead of data may occur, but it cannot supply missing probability or loss estimates.

A news scan found the analysis of aluminum holdout gains. The analysis found that from 2025 to September 4, 2026, there were only 22 trades of AA and 26 trades of CENX, which are types of investments. The average returns were 0.8684% and 0.4291%. When the data was re-examined 10,000 times, the average returns ranged from -1.7738% to 4.2172% and from -2.7594% to 3.7542%, which includes zero, meaning no gain or loss. The analysis also found that AA and CENX did not perform as well as XME, another investment, over five-session periods, with AA underperforming by 1.3465 percentage points and CENX underperforming by 0.3836 percentage points. The daily correlation between AA and CENX was 0.6652, which measures how closely they move together. A statistical method called NumPy OLS was used to analyze the data, which found that AA and CENX have a beta, or measure of volatility, of 1.0600 and 1.1907, respectively. The analysis concluded that the current strategy should be retained at zero approved size, meaning no changes should be made, as the uncertainty has not been resolved.

The antimony profit bridge, which is a way to analyze the relationship between prices and costs, has identified a price-cost squeeze. This means that the difference between the price of antimony and its cost is decreasing. To calculate this, the fund manager used the issuer's Q2 rounded unit prices, costs, and pounds. The change in gross profit was broken down into three parts: volume, price, and cost. The volume part is 428,425 - 340,305 = 88,120 units, multiplied by $8.47, which equals $746,376.40. The price part is 428,425 units multiplied by the difference between $13.70 and $28.32, which equals -$6,263,573.50. The cost part is 428,425 units multiplied by the difference between $19.85 and $13.34, which equals $2,789,046.75. The net result is -$2,728,150.35, which is within $75.65 of the exact amount of -$2,728,226 due to rounding. This information is from the SEC filing at https://www.sec.gov/Archives/edgar/data/101538/000110465926094035/uamy-20260630x10q.htm. The fund manager notes that this analysis is not a causal regression, meaning it does not show cause and effect, and that the mix of factors such as processing timing can affect unit costs. The four Q1/Q2 comparators are not independent statistical evidence, and no quarterly regression is claimed. The inventory of finished antimony is 13,964,876 pounds, and the total inventory of antimony, including unfinished, is 21,380,103 pounds, which is 65.3172% finished. The end finished value divided by the Q2 pure antimony cost of goods sold multiplied by 91 is 222.37 days, which is a descriptive coverage, not a turnover forecast. A news scan found the stock bars data from https://runway.adityaperswal.workers.dev/data/stock-bars, which shows 1,172 daily returns from January 3, 2022, to September 4, 2026. The daily quantiles are -14.3081%, -8.75544%, -0.266698%, 11.42564%, and 19.10013% for the 1%, 5%, 50%, 95%, and 99% levels, respectively. The correlation with the SPY index is 0.2198245, and the beta is 1.24149. This sample includes a period when the stock was a penny stock, and the relationship is not a causal antimony exposure. The fund manager also analyzed the trade holdout, which is a way to test the performance of a trading strategy, and found that the mean confidence interval is [-3.1859, 4.1258]%. The trade block, which is a way to group trades together, has a wider confidence interval of [-3.9867, 4.7908]%. The resampling, which is a way to test the robustness of the results, preserves only short local dependence, not future policy regime. A rejected rule, which is a trading rule that is not accepted, cannot be qualified by a positive arithmetic mean while the compounded return is negative. The live query trades fund, which is a way to analyze the performance of the fund in real-time, shows that there were only two operator liquidations, FRO 3.80516% and STNG 0.75226%, with a mean of 2.27871%, and zero observed loss. The reuse analysis, which is a way to analyze the performance of the fund over time, shows 469 trades, but there are no independent new outcomes. The missing loss and censored exits, which are ways to describe the performance of the fund, make the fund record Kelly undefined. In conclusion, the sources establish a commercial squeeze, not a stock payoff probability, and the approved size is 0.

A review of trades on September 6 found two closed trades, both of which were liquidated by the operator on September 4. Using Python to recalculate the results from the fill prices showed returns of 3.80516296% for FRO and 0.75225677% for STNG, with a mean return of 2.27870986%. No losses were observed, so the average loss and payoff ratio could not be determined. The fact that the same tanker was exposed to both trades and the operator chose when to exit means that the results cannot be interpreted independently. As a result, the 100% win rate that was observed cannot be used to estimate the potential edge of the strategy, and the quarter Kelly criterion is undefined, with a size of 0. The IID Wilson 95 lower bound of 34.238% is only an illustration of sensitivity and not a calibrated bound. It is possible that there is a genuine positive edge despite the small sample size, which would support continuing to collect data, but it does not provide numerical confidence. There are no reference quotes to measure slippage and no natural stop or target outcomes. No new backtest or Monte Carlo simulation was performed, and the previous results, frozen456 and prior439, remain as separate historical and economic evidence. The trade data came from a query of the supply fund using the query_trades function, which returned ids 6 and 3, and the historical bar data came from https://runway.adityaperswal.workers.dev/data/stock-bars.

A news scan found the Sea-Intelligence summary from September 2, which covers July 2026. The reliability was 56.4%, down 6.1 percentage points, and the mean delay was 6.06 days, up 0.59 days, but only for vessels that arrived late. This is called a conditional delay. To understand this, we need to know that conditional means based on a condition, in this case, late arrival. From the same release, we can derive that in June, the reliability was 62.5% and the mean delay was 5.47 days. Using these numbers, we can calculate a diagnostic, which is a measure to help understand the situation, and it increased by 28.81%. This index does not show the full picture of lateness because it does not account for vessels that arrived on time or early, and it does not consider the weight of the vessels. Maersk cited this statistic in a weather notice on September 4, but it is not independent confirmation because it comes from the same origin. Hapag-Lloyd reported different delay ceilings for selected ports on September 3, but this is not a contradiction because it is a different population and period. The collector index labels June as 62.6%, which is 0.1 percentage points different from the inferred 62.5%, and this difference is preserved because it could be due to rounding or revision. The raw data, including AIS, counts, paired arrivals, and spreadsheets, is not available, so no regression, confidence interval, or causal storm coefficient can be calculated from these aggregates. Previously, it was thought that physical disruptions were likely to have a significant impact, and this is corroborated by current carrier restrictions, which strengthen the mechanism, but do not affect the probability of winning stocks. New accepted invoices and independent throughput would strengthen the cash hypothesis, while normalizing delivery metrics would weaken it. The sources for this information are Sea-Intelligence and Maersk and Hapag-Lloyd websites.

A news scan found the text describing a study on trading strategies. The study used a fixed six-sector panel and adjusted daily stock prices from the IEX exchange from 2020 to 2026. The training period was from 2020 to 2023, with usable strategy trades starting in February 2021, and the holdout period was from 2024 to September 4, 2026. The study found that the six daily return series had a mean pair correlation of 0.570715 during training and 0.398842 during the holdout period. The correlation-eigenvalue participation ratio was 2.15466 and 2.87526, which means the descriptive dimensions were not effective for independent trade counts. The baseline had 647 training trades and 598 holdout trades, which clustered in 132 and 126 occupied entry weeks, respectively. The study used a method called bootstrap resampling, which involves re-sampling the data, to test the robustness of the results. The study found that many trades did not result in a positive payoff, with governing four-week mean intervals of -0.21028% to 0.28597% for the baseline, -0.47654% to -0.02183% for the trend, and -0.57805% to -0.02514% for the trend low volatility. A regression analysis was used to examine the relationship between trade returns and factors such as trend and low volatility. The holdout coefficients were 0.86763, -1.16261, -0.96635, and 1.02668, with t-values of 2.804, -3.358, -2.383, and 2.346, respectively. However, the study found that these results were unstable and should not be used to make trading decisions. A strong counterexample was found, with a downtrend and high volatility resulting in a holdout mean of 0.86763% over 150 trades, with a one-week confidence interval of 0.26937% to 1.47896%. The study concluded that there was no untouched evidence to support selecting this particular trading strategy, and it should be recorded as a discovery rather than used for trading. The study's results were limited by the lack of incident labels and the potential for week clusters to share multiweek shocks. The data source was https://runway.adityaperswal.workers.dev/data/stock-bars.

A preregistered note, number 295, analyzed daily training returns from 2023 to 2024, and 250 held-out returns from 2025. The analysis used a 2-factor ordinary least squares method on log returns against the SPY and XLB benchmarks, considering known shock dates from 2026. The holdout predictive R2 was 0.5952 for the linear model and 0.5146 for the APD model, with a root mean square error of 0.7677% and 1.2194% daily. The five-session residual log percentage on March 2, March 19, and July 10 was +0.5278, -1.3739, and -2.0582 for the linear model, and +6.3321, -0.3003, and -1.1084 for the APD model. This analysis is not an executable event backtest because it includes announcement-day close-to-close, related incidents, unknown first-publication minute, and earnings confounds in a 20-day July window. A post-hoc robustness test using the SPY benchmark only changed the linear model results to -3.4158, +1.0627, and -0.8356, and the APD model results to +0.1830, +3.4991, and +0.7979, indicating that attributions depend strongly on the benchmark. State Street's September 3 XLB weights, which were 12.97% for the linear model and 4.76% for the APD model, confirm that the benchmark contains both and cannot be an unaffected control. The weights were not backcast. The conclusion is that no stable causal or equity payoff was identified, and positive physical exposure does not establish a trade edge. The 2025 overlapping placebo windows are a descriptive distribution and not independent nor p-values. No win probability is inferred. The sources of the data are https://runway.adityaperswal.workers.dev/data/stock-bars and https://www.ssga.com/us/en/individual/etfs/state-street-materials-select-sector-spdr-etf-xlb. Every event statistic observation is from 5419 to 5436, and the scripts and results are archived.

A news scan found a report about eight selected bottleneck diagnostics. The report states that eight current-survivor names were selected after a broad discovery, but the selection is not independent economic-alpha confirmation because it sees current winners. The report used a method called NumPy seed 90602 and 10,000 IID bootstrap resamples per ticker of natural net holdout trade returns, which is a way to test the results, and found that 95% percentile intervals all cross zero. The results for each ticker are: AEHR [-22.65, 162.84]%, COHU [-6.25, 33.82]%, AXTI [-26.87, 3755.10]%, MP [-9.28, 83.34]%, MU [-7.56, 81.92]%, Maersk [-5.77, 11.05]%, Lynas [-1.98, 52.82]%, and Fujikura [-21.51, 134.71]%. The report notes that five of the eight fixed training returns are negative, and that the results may understate uncertainty due to tiny sample size, overlapping market regimes, and selection. The report also mentions a statistical method called US 420 daily OLS on SPY, which is a way to measure the relationship between different stocks, and found the following results: beta AEHR 3.447, COHU 1.986, AXTI 2.511, MP 1.416, MU 2.568. However, the report states that these results are not causal supply alpha, which is a measure of a stock's performance. The report also notes that overseas same-date regression is nonsynchronous and local currency, and should be discarded for sizing. Additionally, the report states that two live supply wins were forced operator liquidations, and that there is no natural exit or loss distribution, and slippage is unmeasurable without contemporaneous quote. The prior remains zero eligible edge, which means that there is no advantage, and prospective synchronized evidence with positive cost-adjusted lower bound would increase trade eligibility. The report cites two sources: https://runway.adityaperswal.workers.dev/data/stock-bars and https://query1.finance.yahoo.com/v8/finance/chart/, and query trades fund = supply IDs 3, 6.

The analysis of the GEV payoff is uncertain due to historical data. The study is based on certain assumptions: there are 5 closed holdout trades and no closed training trades. Using a method called NumPy with a seed of 906, the analysis resampled 10,000 times and found the average payoff to be 23.11798%. The range of possible payoffs is between -6.49303% and 58.89909%, which is likely an underestimate. Looking at daily data from 2025 for the SPY stock with 420 aligned returns, the analysis found a beta of 1.78518 and an annual arithmetic intercept of 45.17725%, with a correlation of 0.554865. This data is descriptive, not predictive. The average win of 66.1459% is due to long-term holds and cannot be used to set short-term targets. Two recent fund wins were due to forced liquidations and do not provide a reliable sample. The analysis is cautious and would require a more robust sample with actual risk control to change its eligibility. The data is archived from 2958 to 2997 and is sourced from https://runway.adityaperswal.workers.dev/data/stock-bars. The conclusion is that there is no authorized size that is valid under edgegate and no forecast that GEV will fall.

A news scan found the headline 'Protein payoff uncertainty and descriptive market dependence'. Premises are based on net natural payoffs from a registered test that has small samples and selected current names. The two live supply wins were operator liquidations and contain no loss estimate or reference quotes. A test using NumPy seed 90504 with 10000 draws and circular 3 trade blocks was conducted. The results showed a 95% mean confidence interval for TSN of -1.4654% to 0.2683%, for PPC of -1.5547% to 0.7230%, and for JBS of -0.6432% to 1.7526%. All of these intervals include zero, which means there is no positive lower bound for risk. Block resampling retains local dependence but cannot remove selection or regimes, or unknown tails. A descriptive analysis of 2025 and later stock daily OLS versus SPY used 420 TSN and PPC days, and 308 JBS days. The betas were 0.16053, -0.04664, and 0.28185. The annual arithmetic intercepts were -2.817%, -7.212%, and -0.659%, but these are not causal alpha or policy effects. The prior is cautious, and a replicated prospective fixed rule with actual venue exits and positive net confidence would change it, but repeating the headline won't. A valid deduction is that zero authorized risk is allowed under fund criteria, and soundness is conditional on historical data. The source of this information is https://runway.adityaperswal.workers.dev/data/stock-bars, which includes observations 1723-1737 and saved raw data and code.